Department of Computer Engineering

CE 401 | Course Introduction and Application Information

Course Name

Algorithms Design

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
CE 401	SPRING	3	0	3	5

Prerequisites	CE 221 To succeed (To get a grade of at least DD)
Course Language	English
Course Type	ELECTIVE_COURSE
Course Level	First Cycle
Mode of Delivery	Face-to-face
Teaching Methods and Techniques of the Course	Problem Solving Lecture / Presentation
National Occupational Classification Code	-
Course Coordinator	Prof. Dr. Cem Evrendilek
Course Lecturer(s)	Prof. Dr. Cem Evrendilek
Assistant(s)	-

Course Objectives

The objective of this course is to introduce algorithms by looking at the real-world problems motivating them. Students will be taught a range of design and analysis techniques for problems that arise in computing applications. Greedy algorithms, divide and conquer type of algorithms and dynamic programming will be discussed within the context of different example applications. Approximation algorithms with an emphasis on load balancing and set cover problems will also be covered.

Learning Outcomes

The students who succeeded in this course;

Name	Description	PC Sub	* Contribution Level
Name	Description	PC Sub	1	2	3	4	5
LO1	will be able to classify the different types of algorithms together with their purpose of use.					X
LO2	will be able to explain time and space complexity of different type of algorithms.					X
LO3	will be able to devise efficient greedy algorithms suitable to solve a particular computational problem.					X
LO4	will be able to implement efficient divide and conquer algorithms suitable to solve a particular computational problem.					X
LO5	will be able to formulate efficient dynamic programs suitable to solve a particular optimization problem.					X

Course Description

The course covers basics of Algorithms Analysis, graph theoretic concepts, greedy algorithms, divide and conquer algorithms, dynamic programming, and approximation algorithms.

Related Sustainable Development Goals

Course Category	Core Courses
	Major Area Courses	X
	Supportive Courses
	Media and Managment Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Required Materials	Learning Outcome
1	Introduction: Some Representative Problems	Course Book; Chapter 1	LO1
2	Basics of Algorithms Analysis	Course Book; Chapter 2	LO2
3	Graphs	Course Book; Chapter 3	LO2
4	Greedy Algorithms: Interval Scheduling	Course Book; Chapter 4	LO1
5	Greedy Algorithms: Scheduling to Minimize Lateness	Course Book; Chapter 4	LO3
6	Greedy Algorithms : Minimum-Cost Arborescences	Course Book; Chapter 4	LO3
7	Divide and Conquer: Counting Inversions	Course Book; Chapter 5	LO4
8	Midterm		-
9	Divide and Conquer: Integer Multiplication	Course Book; Chapter 5	LO4
10	Divide and Conquer: Convolutions and The Fast Fourier Transform	Course Book; Chapter 5	LO1
11	Dynamic Programming: Weighted Interval Scheduling	Course Book; Chapter 6	LO1
12	Dynamic Programming: Subset Sums and Knapsacks	Course Book; Chapter 6	LO5
13	Dynamic Programming: Sequence Alignment	Course Book; Chapter 6	LO5
14	Approximation Algorithms: Load Balancing and Set Cover	Course Book; Chapter 11	LO2
15	Semester Review		-
16	Final Exam		-

Course Notes/Textbooks	Algorithm Design; Jon Kleinberg; Éva Tardos; ISBN-10: 0321295358; ISBN-13: 9780321295354; Addison-Wesley; 2005.
Suggested Readings/Materials	Algorithms; Cormen; T.H.; Liesersan; C.E. and Rivest; R.L. ISBN 0-01-013143-0; McGraw-Hill.

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO1	LO2	LO3	LO4	LO5
Homework / Assignments	1	30		X	X	X	X
Midterm	1	30	X	X	X	X
Final Exam	1	40	X	X		X	X
Total	3	100

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Participation	-	-	-
Theoretical Course Hours	16	3	48
Laboratory / Application Hours	-	-	-
Study Hours Out of Class	14	4	56
Field Work	-	-	-
Quizzes / Studio Critiques	-	-	-
Portfolio	-	-	-
Homework / Assignments	4	5	20
Presentation / Jury	-	-	-
Project	-	-	-
Seminar / Workshop	-	-	-
Oral Exams	-	-	-
Midterms	1	12	12
Final Exam	1	14	14
		Total	150

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
			1	2	3	4	5
No program competency data found.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

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